Generalization-based Anonymization

Generalization-Based Anonymization is a data anonymization technique that belongs to the class of privacy-preserving methods by altering original data values, specifically distinguished by replacing specific attribute values with broader, less precise categorical ranges to prevent identity disclosure while maintaining data utility.

80

Publications

6.3K

Citations

201

Authors

106

Institutions

Publications per year

2017–2026

22

Authors

201

  1. XZ
    Xuyun Zhang

    Nanjing University, China

    4 publications · 517 citations

    4

    h-index

  2. NM
    Nikos Mamoulis

    University of Hong Kong, Hong Kong SAR China

    4 publications · 492 citations

    4

    h-index

  3. JC
    Jinjun Chen

    Swinburne University of Technology, Australia

    4 publications · 517 citations

    4

    h-index

  4. XX
    Xiaokui Xiao

    Nanyang Technological University, Singapore

    4 publications · 833 citations

    4

    h-index

  5. YT
    Yufei Tao

    Chinese University of Hong Kong, Hong Kong SAR China

    4 publications · 833 citations

    4

    h-index

1–5 of 201

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 6 publications · 386 citations

    6

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 5 publications · 721 citations

    5

    h-index

  3. BMC Medical Informatics and Decision Making

    2 publications · 88 citations

    2

    h-index

  4. ACM Transactions on Database Systems

    TODS · 2 publications · 90 citations

    2

    h-index

  5. Journal of Computer Science and Technology

    1 publications · 11 citations

    1

    h-index

1–5